Papers with Code 아카이브

3D Face Modelling

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/paperswithcode/paperswithcode-client

Deep Residual Learning for Image Recognition

https://github.com/paradocx/LARNet

Learning Face Representation from Scratch

https://github.com/parilo/rl-server

Continuous control with deep reinforcement learning

https://github.com/parthapratimbanik/facial-keypoint-detection-udacity-ppb

Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet

https://github.com/parvatijay2901/FaceNet_FR

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/parviagrawal/IdenProf

Deep Residual Learning for Image Recognition

https://github.com/pasrichashivam/hyperface_deep_multi-task-learning_keras_implementation

HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition

https://github.com/pat-coady/tiny_imagenet

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/pat-coady/trpo

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/patelmiteshn/darknet

Focal Loss for Dense Object Detection

https://github.com/patientzero/timage-icann2019

Deep Residual Learning for Image Recognition

https://github.com/patrick-batman/Angular-Knowledge-Distillation

ShrinkTeaNet: Million-scale Lightweight Face Recognition via Shrinking Teacher-Student Networks

https://github.com/patrick013/Image-Classification-CNN-and-VGG

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/patrickhart/jaxdl

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/paulblum00/ResNet-Recreation

Deep Residual Learning for Image Recognition

https://github.com/pauldaoudi/food

Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

https://github.com/paulorauber/nn

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/pawelbeza/ClothesDetectorModel

Deep Residual Learning for Image Recognition

https://github.com/pbaylies/stylegan-encoder

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/pcchenxi/lapo-offlienrl

Latent-Variable Advantage-Weighted Policy Optimization for Offline RL

https://github.com/pedrobvidal/insightface

Sample and Computation Redistribution for Efficient Face Detection

https://github.com/peisuke/awr

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

https://github.com/peiyunh/tiny

Finding Tiny Faces

https://github.com/pengrenlai/RetinaNet-tensorflow

Focal Loss for Dense Object Detection

https://github.com/pengsongyou/OMG-ADSC

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

https://github.com/penincillin/DREAM

Pose-Robust Face Recognition via Deep Residual Equivariant Mapping

https://github.com/peterhan91/thorax_gan

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/petermankowski510/facenet_test

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/peteryuX/arcface-tf2

Deep Residual Learning for Image Recognition

https://github.com/peteryuX/esrgan-tf2

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/peteryuX/retinaface-tf2

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/petrum01/Capstone_project_object_detection

Focal Loss for Dense Object Detection

https://github.com/petsol/ContinuousControl_UnityAgent_DDPG_Udacity

Continuous control with deep reinforcement learning

https://github.com/pgtinsley/insightface_oge

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/phamquiluan/ResidualMaskingNetwork

Challenges in Representation Learning: A report on three machine learning contests

https://github.com/phillity/BioCapsule

Deep Residual Learning for Image Recognition

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.